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Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization

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Evolutionary Multi-Criterion Optimization (EMO 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4403))

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Abstract

Research within the area of Evolutionary Multi-objective Optimization (EMO) focused on two- and three-dimensional objective functions, so far. Most algorithms have been developed for and tested on this limited application area. To broaden the insight in the behavior of EMO algorithms (EMOA) in higher dimensional objective spaces, a comprehensive benchmarking is presented, featuring several state-of-the-art EMOA, as well as an aggregative approach and a restart strategy on established scalable test problems with three to six objectives. It is demonstrated why the performance of well-established EMOA (NSGA-II, SPEA2) rapidly degradates with increasing dimension. Newer EMOA like ε-MOEA, MSOPS, IBEA and SMS-EMOA cope very well with high-dimensional objective spaces. Their specific advantages and drawbacks are illustrated, thus giving valuable hints for practitioners which EMOA to choose depending on the optimization scenario. Additionally, a new method for the generation of weight vectors usable in aggregation methods is presented.

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Author information

Authors and Affiliations

  1. Institut für Spanende Fertigung (ISF),  

    Tobias Wagner

  2. Chair of Algorithm Engineering, University of Dortmund, 44221 Dortmund, Germany

    Nicola Beume & Boris Naujoks

Authors
  1. Tobias Wagner
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  2. Nicola Beume
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  3. Boris Naujoks
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Editor information

Shigeru Obayashi Kalyanmoy Deb Carlo Poloni Tomoyuki Hiroyasu Tadahiko Murata

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© 2007 Springer Berlin Heidelberg

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Wagner, T., Beume, N., Naujoks, B. (2007). Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_56

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  • DOI: https://doi.org/10.1007/978-3-540-70928-2_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70927-5

  • Online ISBN: 978-3-540-70928-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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